This invention generally relates to borehole logging and logging-while-drilling (LWD), and in particular to a system and method for determining the properties of layered geological formations. A specific embodiment relates to a system and method for determining layer composition using a high-resolution log, and deriving associated high-resolution formation layer properties using time-dependent data, such as nuclear magnetic resonance (NMR) logs. In addition, or as an alternative, neural networks can be used to generate finer resolution data.
In oil and gas exploration it is always desirable to understand the structure and properties of the geological formation surrounding a borehole, in order to determine if the formation contains hydrocarbon resources (oil and/or gas), to estimate the amount and producibility of hydrocarbon contained in the formation, and to evaluate the best options for completing the well in production. A significant aid in this evaluation is the use of wireline logging and/or logging-while-drilling (LWD) measurements of the formation surrounding the borehole (referred to collectively as xe2x80x9clogsxe2x80x9d or xe2x80x9clog measurementsxe2x80x9d). Typically, one or more logging tools are lowered into the borehole and the tool readings or measurement logs are recorded as the tools traverse the borehole. These measurement logs are used to infer the desired formation properties.
In evaluating the hydrocarbon production potential of a subsurface formation, the formation is described in terms of a set of xe2x80x9cpetrophysical properties.xe2x80x9d Such properties may include: (1) the lithology or the rock type, e.g., amount of sand, shale, limestone, or more detailed mineralogical description, (2) the porosity or fraction of the rock that is void or pore space, (3) the fluid saturations or fractions of the pore space occupied by oil, water and gas, and others. Wireline logging tools do not directly measure petrophysical properties, they measure xe2x80x9clog propertiesxe2x80x9d, for example, bulk density, electrical resistivity, acoustic velocity, or nuclear magnetic resonance (NMR) decay. Log properties are related to petrophysical properties via a set of mathematical or statistical relations, which are generally known in the art. In practice, frequently several different logging tools are combined and used simultaneously to obtain an integrated set of measurements. Thus, different tools may be used to obtain information about the same set of formation properties using different techniques, or different tools may be used to obtain information about different formation properties. Due to differences in physical measurement mechanisms and other factors, different logging tools have different volumes or zones of investigation, hence different measurement resolutions.
Subsurface formations are generally heterogeneous, so that porosity, saturation and lithology vary with position. A common example of heterogeneity is the presence in the formation of geological layers, or beds. Because logging tools have a nonzero volume of investigation, more than one layer may lie within the volume of investigation of a tool. In such cases, the petrophysical evaluation of one layer may be distorted by the presence of another layer falling within the larger volume of investigation of the tool.
The above phenomenon leads to a specific problem in the analysis of subsurface formations that include one or more underground layers, especially when the layers are thin compared with the vertical resolution of the measuring tool. Relatively thin layers (for example, less than about one foot) frequently come in groups of sometimes hundreds of layers, and have become subject to significant commercial interest because of their production potential. Any knowledge about the composition and properties of such layered formations that helps better estimate their production potential has thus become increasingly valuable.
As noted above, however, many of the standard wireline and LWD logs record data with a resolution along the borehole (generally this is the vertical resolution)xe2x80x94that is coarser than the geological layering of the formation. The effect of having low-resolution logs is that an interpretation of the data tends to be an average description of the formation that can seriously mislead the users of the interpretation. This xe2x80x9caveragingxe2x80x9d presents a particular problem in formations that contain a small fraction of thin, highly permeable and porous sand layers incorporated within a formation that consists predominantly of low permeability silts or essentially impermeable shales. In such formations the log properties that would reveal the sand layers are dominated, and thus masked, by the opposite log properties of the silts and shales. Accordingly, the averaging which is due to the low resolution of the measuring tool leads to underestimating of the production potential of the formation.
For example, many of the low resistivity and low contrast pay sands are known to be thinly laminated sand/silt or shale sequences. Most commercially available resistivity logging tools have coarse vertical resolution and fail to read resistivity of individual sand or shale layers. Instead, they read only the averaged horizontal resistivity that is low and dominated by the high conductivity of silt and shale layers, although individual sand layers can be highly resistive. As a result, the low resistivity of the layer sequence may be incorrectly interpreted as poor hydrocarbon potential of the formation.
Similar difficulty exists for NMR logging in thinly laminated formations. NMR logging tools have proved very useful in formation evaluation. Tools of this type include, for example, the centralized MRIL(copyright) tool made by NUMAR Corporation, a Halliburton company, and the sidewall CMR tool made by Schlumberger. The MRILL tool is described, for example, in U.S. Pat. No. 4,710,713 to Taicher et al. and in various other publications including: xe2x80x9cSpin Echo Magnetic Resonance Logging: Porosity and Free Fluid Index Determination,xe2x80x9d by Miller, Paltiel, Gillen, Granot and Bouton, SPE 20561, 65th Annual Technical Conference of the SPE, New Orleans, La., Sep. 23-26, 1990; xe2x80x9cImproved Log Quality With a Dual-Frequency Pulsed NMR Tool,xe2x80x9d by Chandler, Drack, Miller and Prammer, SPE 28365, 69th Annual Technical Conference of the SPE, New Orleans, La., Sep. 25-28, 1994. Certain details of the structure and the use of the MRIL(copyright) tool, as well as the interpretation of various measurement parameters are also discussed in U.S. Pat. Nos. 4,717,876; 4,717,877; 4,717,878; 5,212,447; 5,280,243; 5,309,098; 5,412,320; 5,517,115, 5,557,200; 5,696,448; 5,936,405, 6,005,389 and 6,023,164. The structure and operation of the Schlumberger CMR tool is described, for example, in U.S. Pat. Nos. 4,939,648; 5,055,787 and 5,055,788 and further in xe2x80x9cNovel NMR Apparatus for Investigating an External Sample,xe2x80x9d by Kleinberg, Sezginer and Griffin, J. Magn. Reson. 97, 466-485, 1992. The content of the above patents is hereby expressly incorporated by reference for all purposes, and all non-patent references are incorporated by reference for background.
Generally, although the NMR tools respond only to a very limited zone in the radial direction, their vertical resolution is not sharp enough to identify individual layers. The vertical resolution of the tools gets worse when NMR echo-trains are stacked over multiple events to improve the signal-to-noise ratio (SNR). Consequently, the echo-trains obtained from NMR logging reflect the average properties of the laminated sequences, so that the properties of non-productive silt or shale layers may mask those of productive sand layers.
One way to address the issue of layering identification is by visual inspection of cores, which inspection can identify the boundaries for different layers. Further, there are several logging tools that are capable of identifying the geological layering and classifying the various layer types. These are imaging tools, such as the Borehole Televiewer (BHTV), CAST, the Electric Micro Imaging (EMI) Tool, the Formation Micro Scanning (FMS) Tool, and others. Similarly, other fine resolution tools such as a dipmeter, high resolution Pe log, and high frequency dielectric log HFDT, may also be used to provide high-resolution layering information, when laminae are thicker than a few inches. It has been determined that the EMI tool, for example, is capable of identifing the geological layering and also, with some care, of classifying the various layer types.
Having available geological information from high vertical resolution logs, it is desirable to estimate sand properties out of the averaged measurements. In the case of echo-train data from a NMR logging tool, it is desirable to determine the echo-trains specific to particular lithological types, which can then be used to estimate the lithology-specific T2-distribution for the formation layers. Using well-known mathematical transformations one can then obtain much more accurate permeability estimates for each lithology type, and thus obtain a realistic evaluation of the producibility potential of the formation. Such high-resolution estimates are very desirable, because otherwise the producibility potential of certain laminated formations, which may appear to have low permeability, may be overlooked.
Several attempts have been made in the prior art to address the issue of improving the vertical resolution of certain logs. With reference to FIG. 1, for example, if the formation can be classified into a number of discrete layer types, then a discrete log xe2x80x9cGxe2x80x9d can be created that describes the lithologic layering. In this particular case, an electric micro imaging (EMI) log of the geological formation is assumed, illustrated in the left-most track in FIG. 1. The geological description provided by the EMI log can be processed to obtain image resistivity (second track from the left), to which appropriate thresholds can be applied as to obtain the discrete log G. In the example illustrated in FIG. 1, the discrete log G corresponds to a three-component layering model, consisting of clay, silt and sand. Assuming next that each layer is assigned a property-value [P], mathematically one can create a high-resolution log that describes the true formation property, denoted in a vector notation as GxP. Next, if there is a logging tool that responds linearly to the formation properties, i.e., if the tool response can be modeled as a convolution filter [F], one can create a theoretical log [T] for this tool, given by the expression:
T=F{circle around (X)}(Gxc3x97P),
where {circle around (X)} denotes the convolution operation. Since the process defined in the above expression is linear, it can be re-arranged as follows:
T=Lxc3x97P; where L=F{circle around (X)}G,
in which the result of the convolution term L can be regarded as the lithology actually xe2x80x9cseenxe2x80x9d, or xe2x80x9cinterrogatedxe2x80x9d by the logging tool, which is illustrated in the next-to-last track in FIG. 1.
Assuming that the properties of identically classified layers remain substantially constant over an interval of interest, it is possible to estimate property values Pi for the layers by matching the theoretical log as closely as possible to a measured log [M], as illustrated in the last track in FIG. 1. In practice, this means solving for the properties vector P in the equation:
M=Lxc3x97P.
As seen in the last track in FIG. 1, the theoretical log T, matches quite closely the actual measurement log M over most of the interval of interest for the illustrated example. It should be noted that in practical applications for this process to work with accuracy, the formation interval being characterized must exhibit significant variations in the fractions of the lithologic-types seen by the log.
In accordance with the approach outlined above, the following method has been proposed by one of the co-inventors of this application for use in identifying log properties of the individual layer-types:
1. Classify the lithologies into lithology-types using a high-resolution (EMI-type) log;
2. Create a high-resolution lithology log [G] in which each depth is assigned membership to one lithology-type;
3. Create of a xe2x80x9cconvolution filterxe2x80x9d [F] appropriate to the vertical resolution of the log whose layer-properties one wishes to determine;
4. Create of the log-specific lithology log [L] by convolving the high-resolution lithology-log with the log-specific convolution filter [L=F{circle around (X)}G]; and
5. Estimate the layer-properties [P] over an interval of interest from a best-fit match between the measured log data [M] and the theoretical log constructed from the filtered lithologiesxe2x80x94e.g., by solving M=Lxc3x97P to determine P.
In a least-square sense, the solution to the equation M=Lxc3x97P can be found by determining the values of Pj that minimize the error function:       ∑          n      =      1        N    ⁢      xe2x80x83    ⁢            [                        M          n                -                              ∑                          j              =              1                        J                    ⁢                      xe2x80x83                    ⁢                      {                                          L                nj                            ·                              P                j                                      }                              ]        2  
where Mn is the value of the actual measurement at a specific depth in the logged interval (l,N), Lnj is the fraction of the lithology-type seen at this depth, and Pj is the property-value of this lithology-type. Refinements to this process can be made, such that the layer properties are allowed to vary within the larger interval in order to ensure a better match between the theoretical and the measured logs.
Once the values of the properties vector P have been determined, it is trivial to create a high-resolution version of the theoretical logged property, using the expression T=Lxc3x97P. At the user""s discretion, a log can be created at different resolution by applying a user-defined filter to T.
An alternative method employing an iterative method that minimizes the error between measured logs and predicted logs computed by a forward model is described in U.S. Pat. No. 5,675,147 to Ekstrom et al. The content of this patent is hereby incorporated by reference for all purposes.
While prior art methods, such as discussed above, address to some extent the issues associated with generating high-resolution petrophysical maps, they have a number of drawbacks, some of which are discussed below, and more specifically fail to exploit information available from time-dependent log sequences, such as NMR logs. In addition, the prior art fails to utilize the potential of certain processing techniques, such as the use of neural networks, to enhance the resolution of log data.
The above limitations of current formation evaluation techniques are addressed by the system and method of the present invention, which uses a set of measured wireline and/or LWD logs and, knowledge of the associated tool response models to estimate various properties of a geologic formation of interest. In the important case of a stack of thinly layered formations, the layer compositions and the formation properties inside each layer are estimated in a computationally efficient manner, using time-dependent logs. In one important aspect, the present invention is used to improve the vertical resolution of time-dependent logs, such a NMR logs.
In particular, according to this invention a system and method are proposed for the interpretation of NMR echo-train data. Because of the improved vertical resolution, the method is especially suitable for the formation evaluation of thinly laminated sequences. To this end, geological information is obtained at a high vertical resolution, which is then used to enhance the vertical resolution of the echo-train data. In a preferred embodiment, an Electric Micro Imaging (EMI) tool can be used to provide such information, although various different approaches can be used in alternative embodiments. In accordance with the invention, the high-resolution geological information is used to provide a model of the a time-dependent log data, such as a NMR log of the formation, and is compared with an actual measurement log in order to estimate lithology-specific data representations, such as the typical T2-spectra of each lithological laminae. The lithology-specific data representations are then used to obtain petrophysical parameters of the formation, including enhanced permeability estimates in the laminated sequences. The method of this invention is applicable to any temporal data (i.e., time-varying data associated with a particular logging depth) from other logging tools, such as the thermal neutron decay log, and others.
In particular, in one aspect the present invention is a method for determining petrophysical properties of layered geologic formations, comprising: classifying layers in a portion of a geologic formation into two or more discrete layer types; providing numerical data about layer compositions in said portion of the formation using one or more log measurements; inputting provided numerical data to a neural network trained to detect patterns of classified layers; and enhancing the resolution of at least one log measurement using the output of the trained neural network.
In specific embodiments, the method is applied to discrete layer types, which may comprise sand and shale layer types. Generally, the step of providing information about layer compositions is performed using a high-resolution tool, such as an Electric Micro Imaging (EMI) Tool.
In a preferred embodiment, the petrophysical properties of layers that are determined using the method are in the group including but not limited to: permeability, bulk volume irreducible (BVI) and free fluid index (FFI).
In another aspect, the invention is a geological formation interpretation system comprising a system for interpretation of geological formations, comprising a specially programmed computer having: a first memory for storing one or more actual time-dependent measurement logs of a geological formation; a second memory for storing at least one measurement model based on a formation description, said formation description comprising two or more layer compositions; a neural network trainable to recognize patterns of layer compositions; a processor for generating enhanced lithology-specific measurement log data representations corresponding to said two or more geological layer types from an actual time-dependent measurement log of a geological formation processed using the neural network; and a display for communicating to a user the enhanced lithology-specific measurement log data representations.